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6.
Confidential + Proprietary
Metrics to define success - moving away from the gut
Get them right - we can truly build a great product that grows our business
Get them wrong - we can look successful on paper but completely miss the mark

12.
Confidential + ProprietaryConfidential and Proprietary
Jack Welch, Former CEO of GE
“There are only two
sources of competitive
advantage:
The ability to learn more
about our customers
faster than the
competition,
and the ability to turn that
learning into action faster
than the competition.”

13.
Proprietary + Confidential
Customers are NOT created equal
Focus: Who is your customer?

17.
Confidential + Proprietary
● Deal with data early
● Ensure you have a data strategy
● Add a section at the definition stage
● Make it mandatory
● Provide a process, make it consistent
● Over capture and
LABEL!
You’re focused on getting your product live

28.
Confidential + Proprietary
Machine Learning Lifecycle at a Glance
How do I collect, store and
make data available to the
right systems?
How do I understand what data
is required to solve my business
problem?
User
Data Objective
TrainServe
How do I get to a working
model within the period of
time where my objective is
still relevant?
How do I scale prediction
into production systems?
How do I keep my model
relevant with continuously
updated data?

31.
Confidential + Proprietary
A feature in ML is very different from a feature in Product
In ML, a feature is an individual measurable property or
characteristic of a phenomenon being observed.
Choosing informative, discriminating and independent
features is a crucial step for effective algorithms in
pattern recognition, classification and regression.
Feature Engineering

32.
Confidential + Proprietary
A feature is a data point, so what is good?
Represent raw data in a form conducive for ML
1. Should be related to the objective
2. Should be known at production-time
3. Has to be numeric with meaningful magnitude
4. Has enough examples (absolute minimum of 5)

33.
Confidential + Proprietary
What can I do today to plan for ML
1. Find your Data Strategy and Governance owners –
get familiar with it or create it!
2. Identify the decisions your product makes today.
3. Consider suitability for automation with ML.
4. What data do you have today and what do you need
to capture?
5. Capture data in line with your strategy and
governance guidelines – update them if necessary.
6. Capture LOTS of data, but LABEL it well and
consistently!

34.
Confidential + Proprietary
Takeaway 1 Takeaway 2
Value is in
use of data
Think
inside, outside
& future
It’s what we do with the data that matters
BUT… early consideration can increase value
How does you relate to your surroundings
Relevance, correlation and causation